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In this work, we present a novel human-in-the-loop framework to help thehuman user understand the decision making process that involves choosingpreferred options. We focus on qualitative preference models over alternativesfrom combinatorial domains. This framework is interactive: the user providesher behavioral data to the framework, and the framework explains the learnedmodel to the user. It is iterative: the framework collects feedback on thelearned model from the user and tries to improve it accordingly till the userterminates the iteration. In order to communicate the learned preference modelto the user, we develop visualization of intuitive and explainable graphicmodels, such as lexicographic preference trees and forests, and conditionalpreference networks. To this end, we discuss key aspects of our framework forlexicographic preference models.

Human-In-The-Loop Learning of Qualitative Preference Models